Bioinformatics pipeline to guide late-onset Alzheimer's disease (LOAD) post-GWAS studies: Prioritizing transcription...

As new late-onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell-type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single-cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD-genome-wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD-GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding.

Active enhancers within LOAD-associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP-seq signals. Finally, the high-priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease-relevant tissues.

We catalogued 61 strong enhancers in LOAD-GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy-seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF-binding sites showed ChIP-seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL.

This study provides a framework to catalogue noncoding variations in enhancers located in LOAD-GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single-cell multi-omics assays and gene editing.